Summary

This project seeks to improve on the Howard et al. (2020) methods used to estimate sport fish harvest, catches and releases of rockfish in Alaska waters. This is essentially a Bayesian version of the Howard methods that allows for more appropriate and defensible sharing of information between areas, handles missing data in a more appropriate manor, accurately propagates uncertainty throughout the estimation procedure and thus does not rely on the decision tree approach in the original Howard methods. Furthermore, the Bayesian approach should provide sport fish harvest, catch and mortality estimates back to 1978 when the SWHS was implemented. Harvest estimates should be mostly consistent with Howard estimates during contemporary times, but may differ based on more appropriate weighting of SWHS and logbook data, including estimating and correcting bias in the SWHS data. Furthermore, the Howard methods are wholly reliant on logbook release estimates and ignore the release estimates from the SWHS data (inferred from the catch and harvest estimates). Here we explore several models that attempt to balance all of the data in estimating releases.

Data

Harvest data was available for 22 commercial fishing management areas in Southcentral and Southeast Alaska. Areas with negligible rockfish harvest were pooled with adjacent areas for analysis. Specifically the Aleutian and Bering areas were pooled into an area labeled BSAI; the IBS and EKYT were pooled into an area labeled EKYKT; the Southeast, Southwest, SAKPEN and Chignik areas were pooled into an area labeled SOKO2PEN and the Westside and Mainland areas were pooled into an area labeled WKMA.

Stateside Harvest Survey (SWHS)

Statewide harvest survey estimates of rockfish catch and harvest are available for 28 years (1996-2023) for all users and for 13 years (2011-2023) for guided anglers (Figure 0). Additionally, there are estimates from 1977- 1995 that required some partitioning work to ascribe to current management units. Harvests in unknown areas were apportioned based on harvest proportions in 1996. Variance estimates are not available for pre-1996 data and as such, the maximum observed coefficient of variation (cv) in each commercial fisheries management unit was applied.

**Figure 0.**- Data sources for estimating rockfish harvests and releases in ADF&G commercial fisheries management units.

Figure 0.- Data sources for estimating rockfish harvests and releases in ADF&G commercial fisheries management units.


SWHS estimates are believed to be biased to some degree. These modelling efforts aim to estimate and correct for that bias with the assumption that logbook harvests are a census of guided harvests.

Rockfish release estimates are inferred from the difference between catch and harvest estimates.

Adam noted that the first 5 years (23 years counting the historical data) in the SWHS data set for PWSO seem unreasonable (close to zero and not corroborated with logbook estimates). Adam recommended setting these harvests to unknown, but current model development has included the data. Once a satisfactory model has been identified we will exam the effects of censoring the PWSO data.

Creel Surveys

NA

Guide Logbooks

Sport fishing guides were required to report their harvest of rockfish for 26 years (1998-2023). Reported harvest is also available by assemblage (pelagic vs. non-pelagic). Harvest of yelloweye rockfish were reported separately beginning in 2006.

Logbooks also record the number of rockfish released for the same categories. However, the reliability of the release data is somewhat questionable as reported releases are generally far lower than that estimated by the SWHS. As such several treatments of the data are considered.

Logbook versus SWHS estimates

Estimates of guided harvests and releases from the SWHS do not align with the census from charter logbooks. Logbook harvest reports are generally considered reliable and are used to assess the bias in SWHS reports. However, there is even greater disparity between release estimates in the two sources and it is debatable whether logbook releases should be treated as a census. The Howard et al. (2020) methods do treat the logbook release data as “true” and thus are considerably less than would be estimated from the SWHS data.

**Figure 1.**- SWHS harvest estimates from guided trips (Hhat) versus repoted harvests from charter logbooks (H_lb).

Figure 1.- SWHS harvest estimates from guided trips (Hhat) versus repoted harvests from charter logbooks (H_lb).


The Howard methods treat the logbook release data as a census and then use the ratio of guided:unguided releases in the SWHS to expand the logbook release estimates to generate total and unguided estimates.

To evaluate this discrepancy, several models were used to estimate releases in this exploration. One method (\(LB_{fit}\)) considers the logbook release data to be reliable and a second method (\(LB_{cens}\)) treats the logbook release data as estimates of the minimum released, thus giving more weight to SWHS release estimates. A third method (\(LB_{hyb}\)) is a hybrid approach that treats reported releases of yelloweye as reliable but total rockfish and pelagic rockfish releases as minimums. Model development to date has revealed a tension between the total and pelagic logbook releases and the yelloweye logbook releases.

Composition data

Harvest sampling data exists from Gulf of Alaska areas since 1996 and from Southeast Alaska areas since 2006. Port sampling data is comprised of the number of total rockfish, pelagic and non-pelagic rockfish, black rockfish and yelloweye rockfish.

A current challenge at this juncture is how to accommodate the prohibition on retaining yelloweye in Southeast from 2020 through 2024. Because it is closed to retention the port sampling data is not reflective of releases while remaining an accurate description of the harvest. Current modelling efforts revolve around developing a separate yelloweye curve that censors the missing data.

Process equations

The true harvest \(H_{ay}\) of rockfish for area \(a\) during year \(y\) is assumed to follow a temporal trend defined by a penalized spline:

\[\begin{equation} \textrm{log}(H_{ay})~\sim~\textrm{Normal}(f(a,y), {\sigma_H}) \end{equation}\]

where \(f(a,y)\) in a p-spline basis with 7 components (knots) and a second degree penalty. The variance, \(\sigma_H\), was given a normal prior with a mean and standard deviation of 0.25 and 1, respectively.

Charter and private harvest \(H_{ayu}\) (where u = 1 for charter anglers and u = 2 for private anglers) is a fraction of total annual harvest in each area:

\[\begin{equation} H_{ay1}~=~H_{ay}P_{(user)ay1}\\H_{ay2}~=~H_{ay}(1-P_{(user)ay1}) \end{equation}\]

where \(P_{(user)ay1}\) is the fraction of the annual harvest in each area taken by charter anglers. \(P_{(user)ay1}\) was modeled hierarchically across years as:

\[\begin{equation} P_{(user)ay1}~\sim~\textrm{beta}(\lambda1_a, \lambda2_a) \end{equation}\]

with non-informative priors on both parameters.

Annual black rockfish harvest \(H_{(black)ayu}\) for each area and user group is:

\[\begin{equation} H_{(black)ayu}~=~H_{ayu}P_{(pelagic)ayu}P_{(black|pelagic)ayu} \end{equation}\]

where \(P_{(pelagic)ayu}\) is the fraction of the annual harvest for each area and user group that was pelagic rockfish and \(P_{(black|pelagic)ayu}\) is the fraction of the annual harvest of pelagic rockfish for each area and user group that was black rockfish. Annual yelloweye rockfish harvest \(H_{(yelloweye)ayu}\) for each area and user group:

\[\begin{equation} H_{(yelloweye)ayu}~=~H_{ayu}(1-P_{(pelagic)ayu})P_{(yelloweye|non-pelagic)ayu} \end{equation}\]

where \(P_{(yellow|non-pelagic)ayu}\) is the fraction of the annual harvest of non-pelagic rockfish for each area and user group that was yelloweye rockfish. The composition parameters (\(P_{(pelagic)ayu}\), \(P_{(black|pelagic)ayu}\), \(P_{(yelloweye|non-pelagic)ayu}\)) were modeled using a logistic curve that would allow hindcasting without extrapolating beyond the limit of observed values such that:

\[\begin{equation} \textrm{logit}(P_{(comp)ayu})~=~\beta1_{(comp)ayu} + \frac{\beta2_{(comp)ayu}}{(1 + exp(\beta3_{(comp)ayu}*(y - \beta4_{(comp)ayu})))} + \beta5_{(comp)ayu}*I(u=private)+re_{(comp)ayu} \end{equation}\]

where the \(\beta\) parameters define the intercept, scaling factor, slope, inflection point and private angler effect, respectively, \(y\) is the year index, \(I(u=private)\) is an index variable which is 1 when the user groups is private and 0 otherwise and \(re_{(comp)ayu}\) is a random effect with a non-informative prior.

The true number of released rockfish \(R_{ayu}\) were based on the proportion of the total catch harvested by area, year, user group and species grouping (pelagic or yelloweye), \(pH_{(comp)ayu}\). Thus, converting \(H_{(comp)ayu}\) to total catches by user group, \(C_{(comp)ayu}\), with \(pH_{(comp)ayu}\) results in estimates of total releases such that

\[\begin{equation} R_{(comp)ayu}~=~ C_{(comp)ayu} - H_{(comp)ayu} ~=~ \frac{H_{(comp)ayu}}{pH_{(comp)ayu}} - H_{(comp)ayu} \end{equation}\]

with total releases calculated from the sum of the user group releases. The proportion of total rockfish harvested by user group, \(pH_{ayu}\), was assumed to be the mean of \(pH_{(pelagic)ayu}\), \(pH_{(yelloweye)ayu}\) and \(pH_{(nonpel-nonYE)ayu}\) weighted by the relative harvest \(H_{(comp)ayu}\) such that

\[\begin{equation} R_{ayu}~=~ \frac{\sum ({H_{(comp)ayu} * pH_{(comp)ayu})}}{\sum {H_{(comp)ayu}}} \end{equation}\]

The proportion harvest parameters \(pH_{(pelagic)ayu}\) and \(pH_{(yelloweye)ayu}\) were modeled using a logistic curve that would allow hindcasting based on trends in the data without extrapolating beyond the range of observed values such that

\[\begin{equation} \textrm{logit}(pH_{ayuc})~=~\beta1_{(pH)ayuc} + \frac{\beta2_{(pH)ayuc}}{(1 + exp(\beta3_{(pH)ayuc}*(y - \beta4_{(pH)ayuc}))) + \beta34_{(pH)ayuc}} \end{equation}\]

A random effect term allowed estimation during the historical period when data is available, but the curve defined by the above equation determined release estimates between 1977 and 1990.

\(pH_{(nonpel-nonYE)ayu}\) was modeled separately with an informative prior centered around a \(pH\) of 0.8 such that

\[\begin{equation} pH_{(nonpel-nonYE)ayu}~\sim~\textrm{beta}(in development) \end{equation}\]

Observation equations

SWHS estimates of annual rockfish harvest \(\widehat{SWHS}_H{ay}\) were assumed to index true harvest:

\[\begin{equation} \widehat{SWHS}_H{ay}~\sim~\textrm{LogNormal}\left(\textrm{log}(H_{ay}b_{ay}), \sigma_{SWHSHay}^2\right) \end{equation}\]

where bias in the SWHS harvest estimates \(b_H{ay}\) is modeled hierarchically across years as:

\[\begin{equation} b_H{ay}~\sim~\textrm{Normal}(\mu_H{(b)a}, \sigma_H{(b)a}) \end{equation}\]

with non-informative priors on both parameters.

SWHS estimates of guided angler harvest \(\widehat{SWHS}_H{ay1}\) are related to total harvest by:

\[\begin{equation} \widehat{SWHS}_H{ay1}~\sim~\textrm{LogNormal}\left(\textrm{log}(H_{ay1}b_{ay}), \sigma_{SWHS_{ay1}}^2\right) \end{equation}\]

Reported guide logbook harvest \(\widehat{LB}_H{ay}\) is related to true harvest as:

\[\begin{equation} \widehat{LB}_H{ay}~\sim~\textrm{Poisson}(H_{ay1})\\ \widehat{LB}_H{(pelagic)ay}~\sim~\textrm{Poisson}(H_{ay1}P_{(pelagic)ay1})\\ \widehat{LB}_H{(yelloweye)ay}~\sim~\textrm{Poisson}(H_{(yelloweye)ay1})\\ \end{equation}\]

SWHS estimates of annual rockfish releases \(\widehat{SWHS}_R{ay}\) were assumed to index true releases in a similar fashion and thus modeled similarly. Because logbook release data is more questionable and demonstrates greater disagreement with SWHS estimates (Figure 1), several approaches have been explored. In the first approach model \(LB_{fit}\) treats the release data as a true census and the releases are related to true releases just as harvests were modelled such that:

\[\begin{equation} \widehat{LB}_R{ay}~\sim~\textrm{Poisson}(R_{ay1})\\ \widehat{LB}_R{(pelagic)ay}~\sim~\textrm{Poisson}(R_{ay1}P_{(pelagic)ay1})\\ \widehat{LB}_R{(yelloweye)ay}~\sim~\textrm{Poisson}(R_{(yelloweye)ay1})\\ \end{equation}\]

In the second approach we consider the logbook release data to be a minimal estimate of the true releases. Thus model \(LB_{cens}\) censors the release data (censored data is entered as NA) and treats the reported releases as a minimal number such that

\[\begin{equation} \text{censored} \widehat{LB}_R{ay}~\sim~\textrm{LogNormal}\left(\log(R_{ay}), 1\right)\text{T}\left(\widehat{LB}_R{ay}, \infty\right)\\ \text{censored} \widehat{LB}_R{(pelagic)ay}~\sim~\textrm{LogNormal}\left(\log(R_{(pelagic)ay}), 1\right)\text{T}\left(\widehat{LB}_R{(pelagic)ay}, \infty\right)\\ \text{censored} \widehat{LB}_R{(ye)ay}~\sim~\textrm{LogNormal}\left(\log(R_{(ye)ay}), 1\right)\text{T}\left(\widehat{LB}_R{(ye)ay}, \infty\right)\\ \end{equation}\]

Model \(LB_{hyb}\) is a hybrid approach that treats the yelloweye releases as a reliable census of yelloweye releases (given the emphasis and ease of recording these fish) but censors the pelagic and total rockfish release estimates such that

\[\begin{equation} \text{censored} \widehat{LB}_R{ay}~\sim~\textrm{LogNormal}\left(\log(R_{ay}), 1\right)\text{T}\left(\widehat{LB}_R{ay}, \infty\right) \\ \text{censored} \widehat{LB}_R{(pelagic)ay}~\sim~\textrm{LogNormal}\left(\log(R_{(pelagic)ay}), 1\right)\text{T}\left(\widehat{LB}_R{(pelagic)ay}, \infty\right) \\ \widehat{LB}_R{(yelloweye)ay}~\sim~\textrm{Poisson}(R_{(yelloweye)ay1})\\ \end{equation}\]

SWHS estimates of guided angler release \(\widehat{SWHS}_R{ay1}\) is modeled the same as harvests.

SWHS release bias was modeled differently in the \(LB_{fit}\), \(LB_{cens}\), and \(LB_{hyb}\) models. Because the \(LB_{fit}\) model assumes that logbook release data is true and the poison likelihoods assume a much smaller variance than the large variances associated with the SWHS release estimates, SWHS release estimates \(b_R{ay}\) were modelled independently of the harvest bias \(b_H{ay}\) such that

\[\begin{equation} b_R{ay}~\sim~\textrm{Normal}(\mu_R{(b)a}, \sigma_R{(b)a}) \end{equation}\]

where bias in the SWHS release estimates \(b_R{ay}\) is modeled hierarchically across years as:

\[\begin{equation} b_R{ay}~\sim~\textrm{Normal}(\mu_R{(b)a}, \sigma_R{(b)a}) \end{equation}\]

with non-informative priors on both parameters.

The \(LB_{cens}\) model treats the logbook release data as lower bound on the release estimates and thus the likelihood linking true releases to the SWHS release estimates is dominant. During model development it was apparent that estimating bias in the SWHS data was more difficult and a different structure was employed that assumed bias in SWHS release data followed a similar pattern to that of the harvests but is offset by some area specific amount. In these models \(b_R{ay}\) differed from \(b_H{ay}\) by offset \(Rboff_{a}\) such that

\[\begin{equation} b_R{ay}~=~b_H{ay} + Rboff_{ay} \end{equation}\]

where

\[\begin{equation} Rboff_{a}~\sim~\textrm{Normal}(\mu_{(bR)r}, \sigma_{(bR)r}) \end{equation}\]

such that \(Rboff_{a}\) was modeled hierarchically across region r.

The number of pelagic rockfish sampled in harvest sampling programs \(x_{(pelagic)ayu}\) follow a binomial distribution:

\[\begin{equation} x_{(pelagic)ayu}~\sim~\textrm{Binomial}(P_{(pelagic)ayu}, N_{ayu}) \end{equation}\]

where \(N_{ayu}\) is the total number of rockfish sampled in area \(a\) during year \(y\) form user group \(u\). The number of black rockfish sampled in harvest sampling programs and the number of yellow rockfish sampled modeled analogously with an appropriately substituted \(N\).

Unresolved issues and outstanding questions:

Models detailed in this markdown represent the next step in the modelling process whereby the pH parameters are separated out by species. This approach separates the compositional data that is germaine to the harvests from the release estimates and releases are now based on pH. Additionally, this approach allows the pH parameters to differ between pelagic and yelloweye which is appropriate given regulatory changes as well as fisherman and industry behaviour and is born out in the results. The approach results in great uncertainty around unguided release estimates, but that uncertainty is appropriate given the data. These models handle the yelloweye closures in southeast much more appropriately given that the compositional data is no longer directly applied to the release estimates. These versions of the model are in development and it is unclear whether the \(LB_{cens}\) model would work, but it appears applicable to the \(LB_{fit}\) and \(LB_{hyb}\) approaches.

Other issues include:

  1. Complete convergence has not been achieved and the logistic curve parameters for p_pelagic and p_yellow remain the last sticking point. I think that p_pelagic will resolve with longer chains.
  2. Estimate precision: These models are producing more precise harvest estimates that in Adam’s original model. I am not sure why at this juncture. sigma_H on the spline was switched from a fixed value to a prior centered around that fixed value, but the model estimates are in the same range as the fixed value. Would the number of knots in the spline explain this? 7 knots was settled on during early model fitting when it clearly performed better than fewer or more knots.
  3. Prior choices in general need to be vetted. The priors on the logistic curves are fairly informed in an effort to achieve the desired shapes for hindcasting. Ideally, sensitivity testing would occur but the model is very slow to converge. The beta parameters on the logistic curves have required a lot of work on the priors to reach convergence.
  4. Random effects on pH: These are currently used in the model but because pH isn’t linked to data as the p_comp data is I am not sure what to make of them or if they are appropriate.
  5. Model comparisons: I need to write code for comparing models side by side as well as quantifying the differences between these methods and the Howard methods.

Results

**Figure X.**- Rhat values and proportion of parameters that converged (Rhat < 1.1.)

Figure X.- Rhat values and proportion of parameters that converged (Rhat < 1.1.)

Estimate comparison

Since previous estimates of rockfish harvest have been produced these first 3 graphs will be used to show how the modeled estimates compare to the estimates produced earlier. For total rockfish the estimates are in general agreement although differences are noted. These estimates should be more reliable because they include both SWHS and guide logbook data, handle variance more appropriately, use hierarchical distributions when data is missing, directly consider observation error and are produced using reproducible research.

**Figure 2.**- Total rockfish harvests 1996--2023. Lines and error polygons represent model estimates and points and error bars represent Howard et al estimates.

Figure 2.- Total rockfish harvests 1996–2023. Lines and error polygons represent model estimates and points and error bars represent Howard et al estimates.



**Figure 3.**- Total rockfish releases 1996--2023. Lines and error polygons represent model estimates and points and error bars represent Howard et al estimates.

Figure 3.- Total rockfish releases 1996–2023. Lines and error polygons represent model estimates and points and error bars represent Howard et al estimates.


Notes from Adam: When looking at only black rockfish the most significant differences are for the Prince William Sound Inside area. I did not spend a great deal of time tracking this down although it looks like the previous version used bad values for \(P_{(black)ayu}\) for at least unguided anglers. For the moment I would ignore the results for BSIA and SOKO2SAP. I think it is possible to give approximate values for these areas but it will require a little more coding which I have yet to do.

**Figure 4.**- Black rockfish harvests 1996--2023. Lines and error polygons represent model estimates and points and error bars represent Howard et al estimates.

Figure 4.- Black rockfish harvests 1996–2023. Lines and error polygons represent model estimates and points and error bars represent Howard et al estimates.


And black rockfish releases…

**Figure 5.**- Black rockfish releases 1996--2023. Lines and error polygons represent model estimates and points and error bars represent Howard et al estimates.

Figure 5.- Black rockfish releases 1996–2023. Lines and error polygons represent model estimates and points and error bars represent Howard et al estimates.



**Figure 6.**- Yellow rockfish harvests 1996--2023. Lines and error polygons represent model estimates and points and error bars represent Howard et al estimates.

Figure 6.- Yellow rockfish harvests 1996–2023. Lines and error polygons represent model estimates and points and error bars represent Howard et al estimates.



**Figure 7.**- Yellow rockfish releases 1996--2023. Lines and error polygons represent model estimates and points and error bars represent Howard et al estimates.

Figure 7.- Yellow rockfish releases 1996–2023. Lines and error polygons represent model estimates and points and error bars represent Howard et al estimates.

Model fit

Logbook residuals

**Figure 8.**- Residuals from logbook harvests

Figure 8.- Residuals from logbook harvests


SWHS residuals

**Figure 9.**- Residuals from SWHS harvests.

Figure 9.- Residuals from SWHS harvests.



**Figure 10.**- Residual of SWHS releases

Figure 10.- Residual of SWHS releases

Parameter estimates

P(Charter)

These histograms show the posterior distribution of the mean percent of rockfish harvested by the charter fleet.

**Figure 11.**- Mean percent of harvest by charter anglers.

Figure 11.- Mean percent of harvest by charter anglers.


When considered annually we see the percent of rockfish harvested by the charter fleet follows our data fairly well although we just do not have much information about this ratio. Prior to 2011 the percent charter is confounded with SWHS bias and should be mostly discounted.

**Figure 12.**- Annual estimates of the percent of harvest by charter anglers for 16 commerical fishing manamgent areas, 1996-2023.

Figure 12.- Annual estimates of the percent of harvest by charter anglers for 16 commerical fishing manamgent areas, 1996-2023.

P(Harvest)

These plots show the fitted logistic line to the proportion of caught rockfish that are harvested. These estimates are used for hindcasting catch estimates based on the harvest data in early years when catch estimates are unavailable.

**Figure 13.**- Annual proportion of all rockfish catch that was harvested. Note that pre-1990 estimates are used to estimate catch in these years when catch estimates are not available.

Figure 13.- Annual proportion of all rockfish catch that was harvested. Note that pre-1990 estimates are used to estimate catch in these years when catch estimates are not available.


**Figure 13.**- Annual proportion of pelagic rockfish catch that was harvested. Note that pre-1990 estimates are used to estimate catch in these years when catch estimates are not available.

Figure 13.- Annual proportion of pelagic rockfish catch that was harvested. Note that pre-1990 estimates are used to estimate catch in these years when catch estimates are not available.


**Figure 13.**- Annual proportion of yelloweye rockfish catch that was harvested. Note that pre-1990 estimates are used to estimate catch in these years when catch estimates are not available.

Figure 13.- Annual proportion of yelloweye rockfish catch that was harvested. Note that pre-1990 estimates are used to estimate catch in these years when catch estimates are not available.


**Figure 13.**- Annual proportion of non-pelagic, non-yelloweye rockfish catch that was harvested. Note that pre-1990 estimates are used to estimate catch in these years when catch estimates are not available.

Figure 13.- Annual proportion of non-pelagic, non-yelloweye rockfish catch that was harvested. Note that pre-1990 estimates are used to estimate catch in these years when catch estimates are not available.

SWHS bias

Figure 14 shows the mean estimate for SWHS bias. Cook Inlet, North Gulf Coast and North Southeast Inside all look pretty good while most other areas have substantial bias. Prince William Sound Inside has the largest bias.

**Figure 14.**- Mean SWHS bias for harvests and catches. Note that a bias < 1 indicates that the SWHS *underestimates* the true value and bias > 1 indicates the survey *overestimates* the true value.

Figure 14.- Mean SWHS bias for harvests and catches. Note that a bias < 1 indicates that the SWHS underestimates the true value and bias > 1 indicates the survey overestimates the true value.


Our estimates of SWHS bias track observations fairly well when he have guided harvest estimates. There are some disturbing trends/patterns seen in the earlier time periods. Often the patterns represent periods where SWHS estimates and guide logbook estimates do not follow the recent relationship. I’m not sure what drives the trends but it seems plausible to me that long-term changes in the ratio of charter and private anglers may be a factor. If Charter/Private ratio information is available in the historical creel data it my be helpful here (particularly for North Southeast Inside and South Southeast outside).

**Figure 15.**- Annual estimates of SWHS bias in harvests and releases for 16 commerical fishing manamgent areas, 1996-2023. Note that a bias < 1 indicates that the SWHS *underestimates* the true value and bias > 1 indicates the survey *overestimates* the true value.

Figure 15.- Annual estimates of SWHS bias in harvests and releases for 16 commerical fishing manamgent areas, 1996-2023. Note that a bias < 1 indicates that the SWHS underestimates the true value and bias > 1 indicates the survey overestimates the true value.

P(pelagic)

We model the percentage of pelagic rockfish in the harvest because we have the information for charter anglers (via logbooks) starting in 1998. Other than looking at the model estimates you can use Figure 8 to compare the two data streams for pelagic rockfish harvest. In general they are in agreement with major exceptions in Price William Sound inside, Prince William Sound outside (early in the time series) and South Southeast inside.

**Figure 16.**- Annual estimates of the percent of the sport harvest that was pelagic rockfish for 16 commerical fishing manamgent areas, 1996-2023.

Figure 16.- Annual estimates of the percent of the sport harvest that was pelagic rockfish for 16 commerical fishing manamgent areas, 1996-2023.

P(black|pelagic)

Note that in Southeast Alaska we only have composition data starting in 2006. Tania dug up old SE data, but it did not provide any useful data for species apportionment.

**Figure 17.**- Annual estimates of the percent of the sport harvest of pelagic rockfish that were black rockfish for 16 commerical fishing manamgent areas, 1996-2023.

Figure 17.- Annual estimates of the percent of the sport harvest of pelagic rockfish that were black rockfish for 16 commerical fishing manamgent areas, 1996-2023.

P(yelloweye|non-pelagic)

**Figure 18.**- Annual estimates of the percent of the sport harvest of non-pelagic rockfish that were yelloweye rockfish for 16 commerical fishing manamgent areas, 1996-2023.

Figure 18.- Annual estimates of the percent of the sport harvest of non-pelagic rockfish that were yelloweye rockfish for 16 commerical fishing manamgent areas, 1996-2023.



Summary of unconverged parameters:

Table 1. Summary of unconverged parameters including the number (n) and the average Rhat from the unconverged parameters.
parameter n badRhat_avg
beta3_pH 13 2.530427
beta1_pH 7 2.529979
beta2_pH 12 1.822264
beta0_pH 17 1.678202
beta3_pelagic 5 1.654134
beta0_pelagic 10 1.552120
beta2_pelagic 4 1.536635
tau_beta0_pH 6 1.465655
beta2_yellow 3 1.451318
beta1_pelagic 8 1.427237
beta2_black 3 1.396954
parameter n badRhat_avg
tau_beta0_yellow 1 1.363985
beta3_yellow 1 1.334245
tau_beta0_pelagic 3 1.307008
mu_beta0_pelagic 1 1.288174
mu_beta0_pH 2 1.187995
beta_H 1 1.179828
beta1_yellow 1 1.175844
beta0_yellow 4 1.172665
beta4_pelagic 1 1.145383
beta0_black 1 1.119296
Table 2. Summary of unconverged parameters by area
afognak BSAI CI CSEO eastside EWYKT NG northeast NSEI NSEO PWSI PWSO SOKO2SAP SSEI SSEO WKMA
beta_H 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
beta0_black 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
beta0_pelagic 0 1 0 1 1 1 0 1 1 1 1 1 0 0 0 1
beta0_pH 1 1 0 1 1 1 0 0 1 1 1 0 1 1 1 1
beta0_yellow 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 1
beta1_pelagic 0 0 0 1 1 0 0 1 1 1 1 1 0 0 0 1
beta1_pH 0 0 0 1 1 0 0 0 0 0 0 0 1 0 1 1
beta1_yellow 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
beta2_black 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0
beta2_pelagic 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 1
beta2_pH 1 0 0 1 0 0 1 0 1 0 1 0 1 0 1 1
beta2_yellow 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1
beta3_pelagic 0 0 0 1 1 1 0 0 1 0 0 0 0 0 0 1
beta3_pH 0 0 1 1 1 0 1 0 0 1 1 0 1 1 1 1
beta3_yellow 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
beta4_pelagic 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
mu_beta0_pelagic 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
mu_beta0_pH 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0
tau_beta0_pelagic 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0
tau_beta0_pH 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0
tau_beta0_yellow 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0

Parameter estimates:

Summary Table of Parameter Estimates
Parameter mean sd Lower_CI Median Upper_CI
mu_bc_H[1] -0.120 0.082 -0.266 -0.125 0.051
mu_bc_H[2] -0.103 0.046 -0.180 -0.107 -0.001
mu_bc_H[3] -0.446 0.070 -0.582 -0.447 -0.305
mu_bc_H[4] -0.987 0.187 -1.357 -0.980 -0.638
mu_bc_H[5] 0.761 0.745 -0.212 0.628 2.634
mu_bc_H[6] -2.171 0.326 -2.809 -2.180 -1.528
mu_bc_H[7] -0.472 0.111 -0.697 -0.468 -0.261
mu_bc_H[8] 0.283 0.387 -0.353 0.245 1.147
mu_bc_H[9] -0.299 0.131 -0.556 -0.300 -0.049
mu_bc_H[10] -0.116 0.072 -0.251 -0.118 0.032
mu_bc_H[11] -0.114 0.041 -0.192 -0.115 -0.029
mu_bc_H[12] -0.273 0.107 -0.506 -0.268 -0.065
mu_bc_H[13] -0.115 0.081 -0.270 -0.115 0.047
mu_bc_H[14] -0.286 0.097 -0.483 -0.284 -0.106
mu_bc_H[15] -0.352 0.051 -0.450 -0.354 -0.249
mu_bc_H[16] -0.145 0.382 -0.815 -0.179 0.722
mu_bc_R[1] 1.467 0.184 1.117 1.467 1.828
mu_bc_R[2] 1.496 0.078 1.341 1.498 1.644
mu_bc_R[3] 1.417 0.143 1.117 1.418 1.688
mu_bc_R[4] 0.962 0.222 0.477 0.976 1.352
mu_bc_R[5] 1.206 0.444 0.349 1.205 2.067
mu_bc_R[6] -1.551 0.447 -2.396 -1.550 -0.673
mu_bc_R[7] 0.488 0.220 0.057 0.492 0.901
mu_bc_R[8] 0.534 0.202 0.131 0.531 0.921
mu_bc_R[9] 0.486 0.187 0.076 0.502 0.813
mu_bc_R[10] 1.381 0.180 1.029 1.378 1.738
mu_bc_R[11] 1.199 0.068 1.056 1.203 1.318
mu_bc_R[12] 0.985 0.206 0.525 1.002 1.361
mu_bc_R[13] 1.036 0.091 0.850 1.037 1.214
mu_bc_R[14] 1.066 0.148 0.743 1.072 1.341
mu_bc_R[15] 0.848 0.099 0.654 0.848 1.042
mu_bc_R[16] 1.147 0.126 0.907 1.144 1.397
tau_pH[1] 3.846 0.349 3.188 3.832 4.564
tau_pH[2] 0.931 0.417 0.461 0.775 1.730
tau_pH[3] 3.463 0.424 2.680 3.444 4.353
beta0_pH[1,1] 0.616 0.211 0.204 0.622 1.003
beta0_pH[2,1] 1.187 0.199 0.787 1.194 1.581
beta0_pH[3,1] 1.160 0.291 0.531 1.188 1.637
beta0_pH[4,1] 1.387 0.350 0.647 1.409 1.991
beta0_pH[5,1] -1.288 0.356 -2.044 -1.273 -0.640
beta0_pH[6,1] -0.782 0.733 -2.420 -0.631 0.334
beta0_pH[7,1] -0.529 1.129 -2.400 -0.709 0.920
beta0_pH[8,1] -0.949 0.367 -1.749 -0.908 -0.380
beta0_pH[9,1] -1.393 0.581 -2.596 -1.333 -0.356
beta0_pH[10,1] 0.443 0.194 0.033 0.453 0.800
beta0_pH[11,1] -0.286 0.756 -1.390 -0.506 1.190
beta0_pH[12,1] 0.588 0.207 0.174 0.589 0.988
beta0_pH[13,1] -0.659 0.243 -1.186 -0.637 -0.225
beta0_pH[14,1] -1.120 0.274 -1.655 -1.127 -0.560
beta0_pH[15,1] -1.420 0.361 -2.172 -1.434 -0.704
beta0_pH[16,1] -1.122 0.916 -2.638 -1.175 0.676
beta0_pH[1,2] 2.592 0.453 1.621 2.642 3.265
beta0_pH[2,2] 2.773 0.399 1.700 2.835 3.360
beta0_pH[3,2] 2.573 0.535 1.416 2.604 3.481
beta0_pH[4,2] 2.632 0.358 1.717 2.689 3.199
beta0_pH[5,2] 4.198 2.272 1.628 3.651 9.900
beta0_pH[6,2] 1.712 1.692 -2.199 2.537 3.502
beta0_pH[7,2] 1.270 1.333 -2.294 1.862 2.412
beta0_pH[8,2] 2.476 0.896 -0.508 2.721 3.335
beta0_pH[9,2] 2.945 0.917 0.424 3.141 4.162
beta0_pH[10,2] 3.315 0.823 1.276 3.522 4.403
beta0_pH[11,2] -2.709 0.067 -2.749 -2.733 -2.490
beta0_pH[12,2] -2.707 0.077 -2.750 -2.733 -2.492
beta0_pH[13,2] -2.705 0.079 -2.749 -2.732 -2.479
beta0_pH[14,2] -2.714 0.058 -2.750 -2.733 -2.542
beta0_pH[15,2] -2.708 0.076 -2.749 -2.733 -2.512
beta0_pH[16,2] -2.694 0.126 -2.749 -2.732 -2.357
beta0_pH[1,3] 1.154 0.324 0.375 1.195 1.634
beta0_pH[2,3] 1.612 0.593 0.214 1.666 2.457
beta0_pH[3,3] 2.016 0.302 1.345 2.021 2.622
beta0_pH[4,3] 1.988 0.685 0.571 1.992 3.028
beta0_pH[5,3] 0.443 1.655 -1.541 0.078 4.635
beta0_pH[6,3] -0.401 1.207 -2.437 -0.343 1.911
beta0_pH[7,3] -0.165 0.625 -2.167 0.003 0.524
beta0_pH[8,3] -0.262 0.246 -0.784 -0.247 0.170
beta0_pH[9,3] -0.105 0.581 -1.720 -0.040 0.827
beta0_pH[10,3] -0.039 0.579 -1.542 0.050 0.834
beta0_pH[11,3] 0.105 0.353 -0.600 0.144 0.670
beta0_pH[12,3] -2.579 0.156 -2.745 -2.621 -2.189
beta0_pH[13,3] 0.414 0.328 -0.285 0.441 0.986
beta0_pH[14,3] 0.215 0.258 -0.308 0.227 0.684
beta0_pH[15,3] 0.197 0.311 -0.532 0.238 0.720
beta0_pH[16,3] 0.225 0.296 -0.399 0.240 0.771
beta1_pH[1,1] 3.002 0.457 2.256 2.965 3.930
beta1_pH[2,1] 2.329 0.328 1.729 2.304 3.077
beta1_pH[3,1] 2.561 0.603 1.698 2.446 4.098
beta1_pH[4,1] 3.325 0.841 2.174 3.152 5.489
beta1_pH[5,1] 2.560 0.394 1.807 2.545 3.354
beta1_pH[6,1] 3.958 1.212 1.933 3.818 6.440
beta1_pH[7,1] 2.685 1.391 0.780 2.434 6.273
beta1_pH[8,1] 4.493 1.050 2.837 4.388 6.770
beta1_pH[9,1] 2.941 0.607 1.887 2.872 4.231
beta1_pH[10,1] 2.037 0.284 1.515 2.024 2.643
beta1_pH[11,1] 3.533 0.793 1.991 3.751 4.728
beta1_pH[12,1] 2.399 0.243 1.918 2.398 2.881
beta1_pH[13,1] 3.698 0.335 3.094 3.683 4.446
beta1_pH[14,1] 4.140 0.317 3.509 4.139 4.772
beta1_pH[15,1] 4.100 0.411 3.277 4.111 4.928
beta1_pH[16,1] 5.094 0.962 3.199 5.090 6.992
beta1_pH[1,2] 1.737 1.538 0.070 1.316 5.981
beta1_pH[2,2] 1.870 1.734 0.062 1.302 6.453
beta1_pH[3,2] 1.440 1.056 0.112 1.296 4.466
beta1_pH[4,2] 2.544 1.930 0.133 2.127 7.032
beta1_pH[5,2] 3.005 1.930 0.219 2.743 7.428
beta1_pH[6,2] 2.474 1.555 0.195 2.154 5.863
beta1_pH[7,2] 2.345 1.704 0.093 2.096 6.311
beta1_pH[8,2] 1.789 1.717 0.056 1.220 6.195
beta1_pH[9,2] 1.782 1.429 0.088 1.462 5.559
beta1_pH[10,2] 1.940 1.635 0.091 1.480 6.351
beta1_pH[11,2] 4.134 0.829 2.517 4.478 5.222
beta1_pH[12,2] 4.794 0.431 3.994 4.771 5.720
beta1_pH[13,2] 5.260 0.320 4.624 5.264 5.896
beta1_pH[14,2] 4.641 0.343 3.953 4.639 5.358
beta1_pH[15,2] 5.238 0.308 4.629 5.243 5.874
beta1_pH[16,2] 4.431 0.776 3.291 4.156 5.756
beta1_pH[1,3] 2.011 0.486 1.275 1.948 3.148
beta1_pH[2,3] 1.527 1.273 0.216 1.138 5.345
beta1_pH[3,3] 0.983 0.576 0.267 0.932 1.932
beta1_pH[4,3] 1.499 1.178 0.119 1.285 4.811
beta1_pH[5,3] 4.418 1.809 1.062 4.245 8.424
beta1_pH[6,3] 3.328 1.939 0.275 3.078 7.731
beta1_pH[7,3] 1.663 1.624 0.054 1.055 6.158
beta1_pH[8,3] 3.138 0.403 2.372 3.123 3.929
beta1_pH[9,3] 1.886 0.902 0.348 1.827 3.931
beta1_pH[10,3] 3.312 0.700 2.194 3.219 5.024
beta1_pH[11,3] 2.657 0.417 1.964 2.626 3.471
beta1_pH[12,3] 5.898 0.251 5.381 5.909 6.357
beta1_pH[13,3] 1.695 0.363 1.045 1.672 2.471
beta1_pH[14,3] 2.294 0.315 1.698 2.284 2.933
beta1_pH[15,3] 1.729 0.363 1.096 1.700 2.494
beta1_pH[16,3] 1.616 0.336 0.970 1.618 2.296
beta2_pH[1,1] 0.515 0.327 0.245 0.457 1.062
beta2_pH[2,1] 1.019 0.927 0.241 0.723 3.861
beta2_pH[3,1] 0.808 1.037 0.156 0.461 3.983
beta2_pH[4,1] 0.336 0.254 0.121 0.289 0.786
beta2_pH[5,1] 3.392 1.668 1.102 3.065 7.511
beta2_pH[6,1] 0.287 0.580 0.086 0.187 0.873
beta2_pH[7,1] 0.538 3.795 -6.588 1.468 7.305
beta2_pH[8,1] 0.230 0.098 0.109 0.206 0.484
beta2_pH[9,1] 0.573 0.516 0.222 0.464 1.630
beta2_pH[10,1] 0.967 0.859 0.303 0.711 3.432
beta2_pH[11,1] 1.019 0.784 0.447 0.791 3.466
beta2_pH[12,1] 3.056 1.528 1.152 2.653 6.951
beta2_pH[13,1] 0.699 0.250 0.369 0.650 1.355
beta2_pH[14,1] 1.034 0.428 0.579 0.941 2.042
beta2_pH[15,1] 0.739 0.378 0.389 0.656 1.611
beta2_pH[16,1] 0.316 0.154 0.100 0.297 0.698
beta2_pH[1,2] -0.479 3.199 -6.962 -0.305 5.454
beta2_pH[2,2] -2.172 2.684 -7.869 -1.968 3.492
beta2_pH[3,2] -2.293 2.601 -7.846 -2.120 3.315
beta2_pH[4,2] -2.561 2.471 -7.663 -2.376 2.659
beta2_pH[5,2] 0.128 3.051 -6.675 0.465 5.505
beta2_pH[6,2] -0.426 3.278 -6.861 -0.547 5.791
beta2_pH[7,2] -1.481 3.134 -7.443 -1.576 4.782
beta2_pH[8,2] -1.984 3.092 -7.389 -2.048 4.475
beta2_pH[9,2] -1.617 3.037 -7.472 -1.622 4.720
beta2_pH[10,2] -1.327 3.029 -7.097 -1.330 4.599
beta2_pH[11,2] -2.222 3.839 -8.217 -3.288 5.277
beta2_pH[12,2] -2.290 1.627 -6.494 -1.771 -0.549
beta2_pH[13,2] -3.639 1.775 -7.761 -3.299 -1.198
beta2_pH[14,2] -3.338 1.760 -7.627 -2.916 -0.985
beta2_pH[15,2] -4.707 1.924 -9.029 -4.493 -1.645
beta2_pH[16,2] 0.076 4.340 -8.435 1.496 6.582
beta2_pH[1,3] 2.520 1.781 0.282 2.202 6.787
beta2_pH[2,3] 0.932 2.681 -5.389 0.901 6.313
beta2_pH[3,3] -2.608 2.558 -7.325 -2.673 3.779
beta2_pH[4,3] 1.140 2.644 -5.080 1.076 6.292
beta2_pH[5,3] 2.303 2.203 -1.982 1.952 7.217
beta2_pH[6,3] 2.173 2.510 -3.711 2.057 7.141
beta2_pH[7,3] -0.191 3.099 -6.332 0.067 5.901
beta2_pH[8,3] 3.808 2.090 0.817 3.567 8.344
beta2_pH[9,3] 2.012 2.282 -3.271 1.762 6.890
beta2_pH[10,3] 1.101 1.182 0.274 0.689 4.697
beta2_pH[11,3] -1.528 1.006 -4.345 -1.240 -0.490
beta2_pH[12,3] -1.371 0.330 -2.124 -1.315 -0.905
beta2_pH[13,3] -2.361 1.522 -6.220 -1.968 -0.631
beta2_pH[14,3] -2.772 1.512 -6.580 -2.400 -0.781
beta2_pH[15,3] -2.637 1.617 -6.918 -2.190 -0.620
beta2_pH[16,3] -2.348 1.449 -6.211 -1.980 -0.672
beta3_pH[1,1] 36.130 1.087 34.239 36.059 38.355
beta3_pH[2,1] 32.833 1.117 30.976 32.722 35.363
beta3_pH[3,1] 34.305 1.580 31.894 34.046 38.561
beta3_pH[4,1] 35.655 2.053 32.271 35.410 40.269
beta3_pH[5,1] 27.216 0.426 26.430 27.179 28.052
beta3_pH[6,1] 38.755 3.166 31.713 39.197 43.491
beta3_pH[7,1] 22.563 2.059 19.287 23.441 25.123
beta3_pH[8,1] 40.065 1.814 36.657 39.969 43.530
beta3_pH[9,1] 28.912 1.543 26.434 28.766 32.433
beta3_pH[10,1] 33.231 1.146 31.185 33.193 35.555
beta3_pH[11,1] 30.526 0.824 29.101 30.432 32.367
beta3_pH[12,1] 30.426 0.441 29.607 30.419 31.213
beta3_pH[13,1] 32.732 0.584 31.584 32.711 33.961
beta3_pH[14,1] 31.577 0.444 30.783 31.569 32.524
beta3_pH[15,1] 30.724 0.591 29.564 30.732 31.884
beta3_pH[16,1] 32.144 2.580 29.319 31.301 39.464
beta3_pH[1,2] 31.459 7.773 19.684 29.340 43.293
beta3_pH[2,2] 28.555 5.912 19.588 28.115 41.195
beta3_pH[3,2] 35.481 7.738 20.222 39.776 43.604
beta3_pH[4,2] 27.233 6.279 19.615 25.434 42.136
beta3_pH[5,2] 30.427 6.588 19.854 29.576 42.906
beta3_pH[6,2] 30.860 4.980 21.308 30.035 39.558
beta3_pH[7,2] 26.980 4.850 19.521 26.423 38.246
beta3_pH[8,2] 28.086 5.853 19.550 27.065 41.364
beta3_pH[9,2] 31.706 7.948 19.937 29.582 43.881
beta3_pH[10,2] 29.290 6.387 19.665 28.345 41.829
beta3_pH[11,2] 37.391 8.282 22.144 43.036 43.600
beta3_pH[12,2] 42.108 0.781 40.363 42.164 43.338
beta3_pH[13,2] 43.352 0.323 42.635 43.364 43.905
beta3_pH[14,2] 42.699 0.517 41.501 42.796 43.530
beta3_pH[15,2] 43.324 0.258 42.785 43.328 43.816
beta3_pH[16,2] 31.910 8.598 19.606 29.042 43.635
beta3_pH[1,3] 39.876 1.111 36.706 40.006 41.532
beta3_pH[2,3] 32.059 4.805 20.511 32.747 41.403
beta3_pH[3,3] 39.502 5.079 23.445 41.375 43.197
beta3_pH[4,3] 28.844 4.780 19.643 29.678 39.099
beta3_pH[5,3] 30.261 5.548 19.844 31.276 38.956
beta3_pH[6,3] 30.525 4.761 20.861 30.668 41.605
beta3_pH[7,3] 28.823 6.670 19.502 27.553 42.718
beta3_pH[8,3] 41.333 0.403 40.340 41.376 41.975
beta3_pH[9,3] 32.610 3.866 21.105 33.606 38.380
beta3_pH[10,3] 36.186 1.275 33.376 36.327 38.349
beta3_pH[11,3] 41.443 0.715 40.141 41.374 42.960
beta3_pH[12,3] 42.358 0.248 41.841 42.365 42.837
beta3_pH[13,3] 42.151 0.886 40.565 42.124 43.800
beta3_pH[14,3] 40.822 0.520 39.756 40.867 41.765
beta3_pH[15,3] 41.553 0.702 40.359 41.501 43.131
beta3_pH[16,3] 41.629 0.917 39.836 41.608 43.416
beta0_pelagic[1] 1.200 0.656 0.017 1.200 2.287
beta0_pelagic[2] 0.884 0.452 -0.025 0.889 1.585
beta0_pelagic[3] 0.188 0.348 -0.733 0.255 0.677
beta0_pelagic[4] 0.200 0.428 -1.169 0.263 0.862
beta0_pelagic[5] 0.788 0.493 -0.359 0.817 1.597
beta0_pelagic[6] 1.209 0.434 0.187 1.301 1.798
beta0_pelagic[7] -0.812 2.055 -4.023 -1.547 1.776
beta0_pelagic[8] 1.701 0.316 0.795 1.768 2.112
beta0_pelagic[9] 0.133 1.351 -2.597 0.320 2.121
beta0_pelagic[10] 2.199 0.568 0.777 2.405 2.852
beta0_pelagic[11] 0.010 0.529 -1.366 0.161 0.720
beta0_pelagic[12] 1.661 0.245 1.281 1.686 1.993
beta0_pelagic[13] 0.365 0.313 -0.672 0.435 0.732
beta0_pelagic[14] 0.099 0.358 -1.063 0.172 0.542
beta0_pelagic[15] -0.385 0.155 -0.690 -0.385 -0.081
beta0_pelagic[16] 0.145 0.247 -0.515 0.175 0.540
beta1_pelagic[1] 1.144 0.666 0.069 1.144 2.376
beta1_pelagic[2] 1.000 0.844 0.028 0.786 3.109
beta1_pelagic[3] 0.916 0.555 0.288 0.778 2.869
beta1_pelagic[4] 1.025 0.438 0.389 0.965 2.413
beta1_pelagic[5] 0.935 0.665 0.055 0.856 2.576
beta1_pelagic[6] 0.821 0.875 0.033 0.611 2.951
beta1_pelagic[7] 2.830 1.874 0.063 3.357 5.781
beta1_pelagic[8] 1.052 0.854 0.074 0.816 3.268
beta1_pelagic[9] 2.971 1.343 0.994 2.863 5.628
beta1_pelagic[10] 0.753 0.831 0.015 0.398 2.812
beta1_pelagic[11] 2.714 0.724 1.596 2.584 4.333
beta1_pelagic[12] 2.827 0.455 2.094 2.799 3.661
beta1_pelagic[13] 1.862 0.597 1.118 1.718 3.617
beta1_pelagic[14] 2.743 0.724 1.671 2.639 4.726
beta1_pelagic[15] 2.116 0.280 1.629 2.093 2.677
beta1_pelagic[16] 3.139 0.517 2.245 3.094 4.298
beta2_pelagic[1] 2.001 1.791 -1.623 1.899 6.191
beta2_pelagic[2] 0.883 2.126 -3.643 0.123 5.794
beta2_pelagic[3] 1.770 1.802 0.069 1.115 6.408
beta2_pelagic[4] 2.256 1.750 0.236 1.809 6.472
beta2_pelagic[5] -1.876 2.557 -7.013 -1.762 4.074
beta2_pelagic[6] 1.252 2.759 -4.875 1.197 6.580
beta2_pelagic[7] 1.497 3.797 -6.685 2.405 7.603
beta2_pelagic[8] -1.595 1.806 -6.125 -1.190 0.047
beta2_pelagic[9] 1.054 1.365 0.118 0.502 5.229
beta2_pelagic[10] 0.898 2.649 -4.879 0.566 6.402
beta2_pelagic[11] 0.494 0.791 0.099 0.258 2.774
beta2_pelagic[12] 1.091 0.550 0.390 0.995 2.446
beta2_pelagic[13] 1.347 1.128 0.114 1.120 4.512
beta2_pelagic[14] 0.482 0.420 0.120 0.382 1.494
beta2_pelagic[15] 2.088 1.208 0.792 1.741 5.549
beta2_pelagic[16] 0.512 0.325 0.180 0.421 1.296
beta3_pelagic[1] 23.737 3.754 19.472 22.515 34.465
beta3_pelagic[2] 28.200 5.340 19.598 28.055 38.632
beta3_pelagic[3] 29.896 3.434 23.667 30.012 37.326
beta3_pelagic[4] 25.622 2.303 21.509 25.641 31.418
beta3_pelagic[5] 31.545 4.532 22.286 31.360 38.985
beta3_pelagic[6] 28.622 4.720 20.382 28.309 38.391
beta3_pelagic[7] 23.273 4.786 19.461 20.652 35.258
beta3_pelagic[8] 28.169 4.029 21.056 27.596 37.099
beta3_pelagic[9] 25.237 3.733 19.886 24.529 34.561
beta3_pelagic[10] 27.582 5.162 19.291 27.498 37.548
beta3_pelagic[11] 38.605 3.142 31.658 39.604 41.963
beta3_pelagic[12] 41.759 0.702 41.044 41.896 41.996
beta3_pelagic[13] 40.485 1.719 35.802 40.919 41.950
beta3_pelagic[14] 40.298 1.489 36.361 40.702 41.955
beta3_pelagic[15] 41.761 0.222 41.189 41.826 41.993
beta3_pelagic[16] 41.006 0.995 38.386 41.284 41.975
mu_beta0_pelagic[1] 0.553 0.656 -0.873 0.566 1.709
mu_beta0_pelagic[2] 0.663 1.258 -2.741 1.000 2.091
mu_beta0_pelagic[3] 0.292 0.468 -0.662 0.313 1.122
tau_beta0_pelagic[1] 8.742 16.943 0.075 2.309 65.446
tau_beta0_pelagic[2] 3.964 8.630 0.030 0.509 34.296
tau_beta0_pelagic[3] 1.933 2.092 0.211 1.514 5.710
beta0_yellow[1] -0.480 0.218 -0.989 -0.462 -0.112
beta0_yellow[2] 0.296 0.269 -0.427 0.339 0.676
beta0_yellow[3] -0.394 0.187 -0.794 -0.386 -0.059
beta0_yellow[4] 0.341 0.473 -0.628 0.395 1.082
beta0_yellow[5] -1.666 0.651 -2.705 -1.735 0.263
beta0_yellow[6] -0.031 1.147 -3.076 0.350 1.247
beta0_yellow[7] 1.144 0.989 -1.670 1.530 2.135
beta0_yellow[8] 1.055 0.425 -0.081 1.125 1.625
beta0_yellow[9] 0.132 0.520 -1.050 0.162 0.971
beta0_yellow[10] 0.655 0.203 0.255 0.655 1.063
beta0_yellow[11] -3.936 0.286 -4.513 -3.947 -3.351
beta0_yellow[12] -3.396 0.905 -4.644 -3.692 -1.400
beta0_yellow[13] -4.390 0.314 -4.950 -4.382 -3.825
beta0_yellow[14] -3.988 0.303 -4.604 -3.985 -3.377
beta0_yellow[15] -4.199 0.290 -4.821 -4.169 -3.669
beta0_yellow[16] -3.828 0.333 -4.453 -3.859 -3.063
beta1_yellow[1] 0.475 0.557 0.012 0.307 2.064
beta1_yellow[2] 1.436 0.580 0.778 1.282 3.130
beta1_yellow[3] 0.754 0.299 0.310 0.722 1.527
beta1_yellow[4] 2.468 1.082 0.897 2.340 4.727
beta1_yellow[5] 4.941 1.737 1.910 4.828 8.801
beta1_yellow[6] 2.863 1.594 0.371 2.651 6.750
beta1_yellow[7] 2.095 1.620 0.095 1.740 6.315
beta1_yellow[8] 2.107 1.350 0.327 1.756 5.587
beta1_yellow[9] 1.871 1.062 0.247 1.751 4.509
beta1_yellow[10] 2.445 0.575 1.412 2.410 3.711
beta1_yellow[11] 3.879 0.378 3.157 3.884 4.641
beta1_yellow[12] 3.346 1.854 0.259 2.839 6.937
beta1_yellow[13] 3.728 0.495 2.904 3.683 4.801
beta1_yellow[14] 3.931 0.406 3.166 3.921 4.762
beta1_yellow[15] 3.134 0.371 2.502 3.094 3.916
beta1_yellow[16] 3.601 0.453 2.706 3.601 4.523
beta2_yellow[1] -0.970 2.577 -6.144 -1.016 4.759
beta2_yellow[2] -1.665 1.540 -5.682 -1.243 -0.095
beta2_yellow[3] -2.802 1.708 -6.173 -3.013 -0.126
beta2_yellow[4] -0.568 1.116 -4.363 -0.160 -0.060
beta2_yellow[5] -3.402 1.838 -7.688 -3.102 -0.696
beta2_yellow[6] 1.646 3.145 -5.554 2.042 7.127
beta2_yellow[7] -0.834 2.979 -5.690 -1.173 5.590
beta2_yellow[8] -2.053 2.305 -7.117 -1.536 1.949
beta2_yellow[9] 1.810 2.791 -4.731 1.968 6.798
beta2_yellow[10] -2.957 1.679 -6.910 -2.643 -0.614
beta2_yellow[11] -0.756 0.245 -1.353 -0.718 -0.398
beta2_yellow[12] 1.471 2.373 -1.843 -0.025 6.880
beta2_yellow[13] -0.587 0.239 -1.111 -0.554 -0.261
beta2_yellow[14] -0.856 0.277 -1.515 -0.814 -0.442
beta2_yellow[15] -0.860 0.329 -1.647 -0.794 -0.426
beta2_yellow[16] -0.739 0.257 -1.295 -0.707 -0.376
beta3_yellow[1] 28.648 4.730 20.331 28.637 38.188
beta3_yellow[2] 29.421 2.000 24.918 29.293 33.370
beta3_yellow[3] 32.209 2.030 27.754 32.185 36.472
beta3_yellow[4] 29.889 3.682 22.430 29.855 36.662
beta3_yellow[5] 32.601 1.043 30.576 32.620 34.541
beta3_yellow[6] 33.584 5.557 24.667 33.048 40.894
beta3_yellow[7] 27.697 3.461 21.131 27.773 35.664
beta3_yellow[8] 31.436 3.409 23.102 32.640 37.406
beta3_yellow[9] 34.453 4.239 23.204 36.063 39.089
beta3_yellow[10] 29.169 0.787 27.331 29.280 30.362
beta3_yellow[11] 41.888 0.126 41.559 41.928 41.998
beta3_yellow[12] 31.947 3.313 29.042 30.275 40.533
beta3_yellow[13] 41.593 0.607 40.069 41.781 41.994
beta3_yellow[14] 41.896 0.119 41.586 41.934 41.998
beta3_yellow[15] 41.839 0.182 41.366 41.896 41.996
beta3_yellow[16] 41.886 0.129 41.536 41.928 41.997
mu_beta0_yellow[1] -0.068 0.402 -0.858 -0.072 0.682
mu_beta0_yellow[2] 0.143 0.668 -1.318 0.197 1.276
mu_beta0_yellow[3] -3.866 0.558 -4.740 -3.942 -2.434
tau_beta0_yellow[1] 6.640 9.982 0.270 3.290 38.784
tau_beta0_yellow[2] 1.752 5.195 0.066 0.590 22.167
tau_beta0_yellow[3] 12.041 16.838 0.143 4.544 61.102
beta0_black[1] -0.067 0.166 -0.395 -0.066 0.263
beta0_black[2] 1.653 0.378 0.490 1.738 2.071
beta0_black[3] 1.196 0.239 0.635 1.229 1.541
beta0_black[4] 1.782 0.431 0.522 1.888 2.293
beta0_black[5] 1.404 1.099 -0.397 1.361 3.328
beta0_black[6] 1.437 1.468 -0.443 1.370 3.418
beta0_black[7] 1.389 1.083 -0.617 1.356 3.343
beta0_black[8] 1.166 0.303 0.454 1.203 1.645
beta0_black[9] 1.674 0.521 0.776 1.645 2.630
beta0_black[10] 1.361 0.164 1.055 1.365 1.661
beta0_black[11] 3.317 0.273 2.602 3.354 3.728
beta0_black[12] 4.410 0.209 4.013 4.414 4.781
beta0_black[13] -0.064 0.240 -0.567 -0.053 0.378
beta0_black[14] 1.880 0.639 0.261 2.044 2.703
beta0_black[15] 0.998 0.401 -0.078 1.090 1.514
beta0_black[16] 3.313 1.061 0.366 3.695 4.368
beta2_black[1] 3.083 1.674 0.736 2.789 7.099
beta2_black[2] -1.549 2.471 -6.694 -1.319 4.507
beta2_black[3] -0.030 3.215 -6.242 0.043 6.474
beta2_black[4] -1.589 1.663 -6.007 -1.022 -0.056
beta2_black[5] 0.031 3.091 -5.934 -0.027 6.186
beta2_black[6] -0.049 3.133 -6.302 -0.056 6.058
beta2_black[7] 0.084 3.158 -6.263 0.087 6.083
beta2_black[8] -3.041 2.207 -7.784 -2.809 1.119
beta2_black[9] -1.312 2.518 -6.574 -0.943 4.407
beta2_black[10] -0.913 2.755 -6.187 -1.035 5.331
beta2_black[11] -1.669 2.010 -5.453 -1.442 3.256
beta2_black[12] -3.087 1.675 -7.181 -2.853 -0.703
beta2_black[13] -2.089 1.514 -6.156 -1.607 -0.408
beta2_black[14] -1.085 1.483 -5.389 -0.409 -0.080
beta2_black[15] -1.645 2.075 -6.185 -1.162 2.160
beta2_black[16] 3.142 2.733 -1.552 3.000 8.143
beta3_black[1] 41.801 0.876 40.056 41.893 43.226
beta3_black[2] 29.499 7.986 19.145 29.586 44.288
beta3_black[3] 27.863 7.331 19.237 27.377 44.175
beta3_black[4] 32.864 4.168 21.498 32.967 41.200
beta3_black[5] 31.823 7.283 19.793 31.742 45.053
beta3_black[6] 31.887 7.489 19.564 31.773 45.136
beta3_black[7] 31.723 7.319 19.887 31.688 44.971
beta3_black[8] 28.421 7.830 20.149 23.273 42.982
beta3_black[9] 34.018 8.394 19.548 34.975 45.062
beta3_black[10] 28.724 9.294 19.361 24.497 45.461
beta3_black[11] 33.883 4.411 29.102 32.445 44.876
beta3_black[12] 32.889 0.676 31.356 32.964 33.861
beta3_black[13] 39.194 0.866 37.357 39.303 40.497
beta3_black[14] 37.923 3.691 30.224 38.172 45.100
beta3_black[15] 36.050 5.158 29.148 35.296 45.406
beta3_black[16] 33.978 4.287 29.130 32.734 43.890
beta4_black[1] -0.291 0.210 -0.704 -0.289 0.108
beta4_black[2] 0.279 0.195 -0.117 0.279 0.662
beta4_black[3] -0.997 0.200 -1.390 -0.993 -0.601
beta4_black[4] 0.662 0.234 0.211 0.660 1.128
beta4_black[5] 0.057 3.199 -6.403 0.060 6.210
beta4_black[6] 0.053 3.108 -6.104 0.099 6.153
beta4_black[7] 0.049 3.150 -6.006 0.046 6.227
beta4_black[8] -0.855 0.391 -1.636 -0.860 -0.106
beta4_black[9] 2.125 1.084 0.242 2.041 4.519
beta4_black[10] 0.028 0.198 -0.360 0.029 0.416
beta4_black[11] -0.699 0.235 -1.160 -0.700 -0.234
beta4_black[12] 0.596 0.358 -0.085 0.591 1.331
beta4_black[13] -1.293 0.233 -1.728 -1.295 -0.831
beta4_black[14] -0.038 0.262 -0.546 -0.045 0.483
beta4_black[15] -0.938 0.227 -1.391 -0.941 -0.495
beta4_black[16] -0.581 0.254 -1.072 -0.579 -0.087
mu_beta0_black[1] 1.011 0.857 -1.094 1.079 2.446
mu_beta0_black[2] 1.343 0.582 0.222 1.366 2.266
mu_beta0_black[3] 1.996 1.145 -0.665 2.124 3.735
tau_beta0_black[1] 1.351 1.439 0.052 0.961 4.768
tau_beta0_black[2] 23.370 42.049 0.142 7.133 140.106
tau_beta0_black[3] 0.318 0.220 0.028 0.271 0.849
sigma_H[1] 0.219 0.057 0.122 0.213 0.341
sigma_H[2] 0.174 0.029 0.123 0.172 0.238
sigma_H[3] 0.186 0.042 0.111 0.183 0.278
sigma_H[4] 0.419 0.076 0.294 0.410 0.595
sigma_H[5] 1.031 0.212 0.650 1.022 1.471
sigma_H[6] 0.396 0.204 0.043 0.392 0.828
sigma_H[7] 0.299 0.059 0.208 0.290 0.436
sigma_H[8] 0.413 0.085 0.267 0.405 0.596
sigma_H[9] 0.517 0.120 0.333 0.503 0.800
sigma_H[10] 0.209 0.041 0.137 0.206 0.296
sigma_H[11] 0.273 0.045 0.199 0.269 0.373
sigma_H[12] 0.444 0.165 0.207 0.426 0.777
sigma_H[13] 0.214 0.036 0.153 0.211 0.293
sigma_H[14] 0.512 0.093 0.349 0.506 0.705
sigma_H[15] 0.244 0.040 0.177 0.240 0.334
sigma_H[16] 0.215 0.041 0.146 0.211 0.308
lambda_H[1] 2.667 3.541 0.137 1.458 12.830
lambda_H[2] 8.014 7.580 0.758 5.896 28.233
lambda_H[3] 5.992 9.109 0.273 2.975 30.742
lambda_H[4] 0.006 0.004 0.001 0.005 0.018
lambda_H[5] 3.008 7.450 0.024 0.754 21.374
lambda_H[6] 7.762 15.172 0.009 0.889 53.677
lambda_H[7] 0.016 0.011 0.003 0.013 0.044
lambda_H[8] 8.174 9.910 0.155 4.707 36.690
lambda_H[9] 0.016 0.011 0.003 0.013 0.042
lambda_H[10] 0.291 0.484 0.033 0.194 1.113
lambda_H[11] 0.239 0.360 0.012 0.123 1.104
lambda_H[12] 4.877 6.627 0.176 2.628 23.038
lambda_H[13] 3.157 2.956 0.220 2.351 11.102
lambda_H[14] 3.933 5.272 0.246 2.347 17.325
lambda_H[15] 0.025 0.042 0.003 0.017 0.091
lambda_H[16] 0.652 0.981 0.036 0.331 3.041
mu_lambda_H[1] 4.291 1.913 1.178 4.086 8.485
mu_lambda_H[2] 3.807 1.961 0.547 3.590 8.013
mu_lambda_H[3] 3.528 1.879 0.747 3.245 7.715
sigma_lambda_H[1] 8.539 4.338 1.920 7.901 18.206
sigma_lambda_H[2] 8.271 4.661 0.916 7.701 18.377
sigma_lambda_H[3] 6.420 4.094 1.028 5.546 16.383
beta_H[1,1] 6.832 1.154 3.973 7.016 8.615
beta_H[2,1] 9.887 0.499 8.735 9.913 10.791
beta_H[3,1] 7.980 0.764 6.135 8.083 9.220
beta_H[4,1] 9.271 7.775 -6.541 9.347 24.220
beta_H[5,1] 0.057 2.534 -5.013 0.145 4.695
beta_H[6,1] 3.152 3.931 -6.427 4.521 7.774
beta_H[7,1] 1.996 5.311 -9.622 2.305 11.404
beta_H[8,1] 1.112 2.353 -2.465 1.211 3.355
beta_H[9,1] 13.091 5.581 2.286 13.113 24.372
beta_H[10,1] 7.052 1.714 3.485 7.114 10.307
beta_H[11,1] 5.185 3.376 -2.610 5.793 9.883
beta_H[12,1] 2.665 1.063 0.800 2.609 5.039
beta_H[13,1] 9.042 0.978 6.809 9.125 10.530
beta_H[14,1] 2.222 1.000 0.152 2.259 4.149
beta_H[15,1] -5.963 3.795 -12.764 -6.154 2.701
beta_H[16,1] 3.644 2.832 -0.950 3.255 10.553
beta_H[1,2] 7.898 0.259 7.357 7.905 8.391
beta_H[2,2] 10.030 0.135 9.763 10.032 10.294
beta_H[3,2] 8.964 0.192 8.582 8.966 9.347
beta_H[4,2] 3.559 1.476 0.848 3.507 6.630
beta_H[5,2] 1.950 0.974 -0.013 1.952 3.838
beta_H[6,2] 5.741 1.074 3.230 5.925 7.454
beta_H[7,2] 2.184 1.025 0.377 2.120 4.433
beta_H[8,2] 3.014 0.819 1.506 3.105 4.212
beta_H[9,2] 3.393 1.124 1.243 3.369 5.722
beta_H[10,2] 8.194 0.350 7.474 8.208 8.853
beta_H[11,2] 9.744 0.608 8.823 9.640 11.111
beta_H[12,2] 3.969 0.384 3.251 3.954 4.787
beta_H[13,2] 9.111 0.263 8.626 9.100 9.661
beta_H[14,2] 4.021 0.349 3.353 4.015 4.730
beta_H[15,2] 11.337 0.679 9.890 11.367 12.574
beta_H[16,2] 4.426 0.806 2.941 4.388 6.101
beta_H[1,3] 8.470 0.264 7.985 8.458 9.021
beta_H[2,3] 10.082 0.118 9.852 10.080 10.331
beta_H[3,3] 9.646 0.160 9.337 9.643 9.966
beta_H[4,3] -2.493 0.869 -4.247 -2.490 -0.802
beta_H[5,3] 3.974 0.657 2.606 3.994 5.213
beta_H[6,3] 8.055 1.214 6.385 7.692 10.640
beta_H[7,3] -2.263 0.700 -3.724 -2.240 -0.924
beta_H[8,3] 5.181 0.407 4.604 5.139 6.068
beta_H[9,3] -2.673 0.761 -4.146 -2.661 -1.210
beta_H[10,3] 8.718 0.278 8.193 8.717 9.273
beta_H[11,3] 8.557 0.280 7.966 8.580 9.046
beta_H[12,3] 5.266 0.320 4.542 5.301 5.807
beta_H[13,3] 8.819 0.182 8.456 8.820 9.179
beta_H[14,3] 5.686 0.274 5.057 5.711 6.170
beta_H[15,3] 10.401 0.313 9.804 10.402 11.027
beta_H[16,3] 5.937 0.618 4.654 5.979 7.011
beta_H[1,4] 8.207 0.199 7.759 8.222 8.550
beta_H[2,4] 10.130 0.119 9.870 10.136 10.341
beta_H[3,4] 10.124 0.167 9.758 10.139 10.415
beta_H[4,4] 11.771 0.448 10.847 11.779 12.619
beta_H[5,4] 5.687 0.821 4.333 5.599 7.566
beta_H[6,4] 7.110 0.887 5.091 7.357 8.355
beta_H[7,4] 8.149 0.336 7.472 8.143 8.808
beta_H[8,4] 6.681 0.228 6.240 6.683 7.099
beta_H[9,4] 7.194 0.469 6.292 7.197 8.122
beta_H[10,4] 7.746 0.233 7.332 7.732 8.246
beta_H[11,4] 9.288 0.203 8.896 9.285 9.699
beta_H[12,4] 7.154 0.212 6.749 7.148 7.592
beta_H[13,4] 8.990 0.144 8.697 8.991 9.264
beta_H[14,4] 7.658 0.215 7.237 7.652 8.088
beta_H[15,4] 9.423 0.233 8.958 9.424 9.876
beta_H[16,4] 9.278 0.231 8.861 9.269 9.742
beta_H[1,5] 8.991 0.157 8.678 8.992 9.289
beta_H[2,5] 10.790 0.094 10.614 10.786 10.986
beta_H[3,5] 10.933 0.166 10.641 10.924 11.280
beta_H[4,5] 8.392 0.467 7.526 8.371 9.368
beta_H[5,5] 5.322 0.658 3.735 5.406 6.383
beta_H[6,5] 8.765 0.627 7.877 8.611 10.233
beta_H[7,5] 6.808 0.326 6.158 6.806 7.456
beta_H[8,5] 8.198 0.196 7.841 8.188 8.593
beta_H[9,5] 8.159 0.486 7.162 8.168 9.104
beta_H[10,5] 10.104 0.225 9.640 10.111 10.552
beta_H[11,5] 11.557 0.228 11.105 11.556 12.017
beta_H[12,5] 8.514 0.201 8.121 8.513 8.910
beta_H[13,5] 10.000 0.134 9.738 9.997 10.269
beta_H[14,5] 9.182 0.232 8.760 9.173 9.671
beta_H[15,5] 11.207 0.243 10.730 11.204 11.692
beta_H[16,5] 9.934 0.179 9.569 9.941 10.271
beta_H[1,6] 10.238 0.201 9.882 10.223 10.666
beta_H[2,6] 11.527 0.108 11.318 11.528 11.742
beta_H[3,6] 10.811 0.160 10.476 10.823 11.094
beta_H[4,6] 12.877 0.823 11.209 12.907 14.383
beta_H[5,6] 5.870 0.646 4.656 5.849 7.189
beta_H[6,6] 8.753 0.637 7.046 8.849 9.740
beta_H[7,6] 9.757 0.545 8.680 9.750 10.862
beta_H[8,6] 9.526 0.247 9.042 9.538 9.958
beta_H[9,6] 8.518 0.791 6.923 8.510 10.146
beta_H[10,6] 9.525 0.314 8.840 9.551 10.085
beta_H[11,6] 10.815 0.349 10.040 10.841 11.461
beta_H[12,6] 9.399 0.253 8.899 9.393 9.914
beta_H[13,6] 11.081 0.168 10.788 11.069 11.458
beta_H[14,6] 9.891 0.292 9.311 9.895 10.465
beta_H[15,6] 10.842 0.426 9.990 10.846 11.682
beta_H[16,6] 10.482 0.251 9.921 10.498 10.928
beta_H[1,7] 10.866 0.972 8.572 11.008 12.418
beta_H[2,7] 12.225 0.442 11.314 12.235 13.071
beta_H[3,7] 10.563 0.661 9.065 10.612 11.724
beta_H[4,7] 2.485 4.209 -5.359 2.331 11.038
beta_H[5,7] 6.453 2.072 2.665 6.345 11.498
beta_H[6,7] 9.496 2.300 4.831 9.458 15.179
beta_H[7,7] 10.862 2.788 5.257 10.941 16.269
beta_H[8,7] 10.933 0.862 9.348 10.905 12.547
beta_H[9,7] 4.243 4.075 -4.142 4.359 12.357
beta_H[10,7] 9.920 1.434 7.331 9.823 13.057
beta_H[11,7] 11.082 1.736 7.946 10.948 14.901
beta_H[12,7] 9.986 0.950 7.810 10.065 11.580
beta_H[13,7] 11.652 0.825 9.709 11.757 12.884
beta_H[14,7] 10.534 0.939 8.580 10.600 12.176
beta_H[15,7] 12.290 2.196 7.978 12.274 16.676
beta_H[16,7] 12.541 1.387 10.317 12.390 15.715
beta0_H[1] 9.233 13.743 -19.312 9.119 38.468
beta0_H[2] 10.666 6.208 -2.183 10.685 23.340
beta0_H[3] 9.858 10.440 -10.573 9.969 30.129
beta0_H[4] 11.345 192.100 -374.900 10.319 400.427
beta0_H[5] 3.739 29.567 -57.847 4.134 63.045
beta0_H[6] 8.048 49.963 -96.055 7.856 111.501
beta0_H[7] 5.588 124.224 -239.188 7.227 246.318
beta0_H[8] 6.300 19.960 -13.158 6.635 28.386
beta0_H[9] 7.328 119.906 -228.127 6.820 260.048
beta0_H[10] 8.660 32.913 -58.445 8.295 77.302
beta0_H[11] 10.171 50.114 -91.958 10.401 110.778
beta0_H[12] 6.702 13.020 -18.093 7.007 28.692
beta0_H[13] 9.691 11.878 -12.385 9.988 30.028
beta0_H[14] 6.871 12.510 -14.742 7.103 27.831
beta0_H[15] 11.509 107.987 -217.599 12.234 235.447
beta0_H[16] 7.701 27.578 -51.509 7.718 63.211